Overview

Dataset statistics

Number of variables10
Number of observations599
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.6 KiB
Average record size in memory115.6 B

Variable types

Numeric8
Categorical2

Alerts

Plasma glucose is highly overall correlated with AgeHigh correlation
Blood Work Result-2 is highly overall correlated with Blood Work Result-3High correlation
Blood Work Result-3 is highly overall correlated with Blood Work Result-2High correlation
Age is highly overall correlated with Plasma glucoseHigh correlation
Plasma glucose has 93 (15.5%) zerosZeros
Blood Pressure has 28 (4.7%) zerosZeros
Blood Work Result-2 has 175 (29.2%) zerosZeros
Blood Work Result-3 has 289 (48.2%) zerosZeros
Body mass index has 9 (1.5%) zerosZeros

Reproduction

Analysis started2023-08-01 09:52:31.650099
Analysis finished2023-08-01 09:52:53.213430
Duration21.56 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Plasma glucose
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8247078
Minimum0
Maximum17
Zeros93
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size25.5 KiB
2023-08-01T09:52:53.427715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.362839
Coefficient of variation (CV)0.87924075
Kurtosis0.28990902
Mean3.8247078
Median Absolute Deviation (MAD)2
Skewness0.91400756
Sum2291
Variance11.308686
MonotonicityNot monotonic
2023-08-01T09:52:53.671482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 100
16.7%
0 93
15.5%
2 76
12.7%
3 59
9.8%
4 54
9.0%
5 49
8.2%
7 37
 
6.2%
6 37
 
6.2%
8 32
 
5.3%
9 20
 
3.3%
Other values (7) 42
7.0%
ValueCountFrequency (%)
0 93
15.5%
1 100
16.7%
2 76
12.7%
3 59
9.8%
4 54
9.0%
5 49
8.2%
6 37
 
6.2%
7 37
 
6.2%
8 32
 
5.3%
9 20
 
3.3%
ValueCountFrequency (%)
17 1
 
0.2%
15 1
 
0.2%
14 2
 
0.3%
13 7
 
1.2%
12 8
 
1.3%
11 7
 
1.2%
10 16
2.7%
9 20
3.3%
8 32
5.3%
7 37
6.2%

Blood Work Result-1
Real number (ℝ)

Distinct129
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.15359
Minimum0
Maximum198
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size25.5 KiB
2023-08-01T09:52:53.964370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.9
Q199
median116
Q3140
95-th percentile181
Maximum198
Range198
Interquartile range (IQR)41

Descriptive statistics

Standard deviation32.682364
Coefficient of variation (CV)0.27200489
Kurtosis0.75640184
Mean120.15359
Median Absolute Deviation (MAD)21
Skewness0.11617993
Sum71972
Variance1068.1369
MonotonicityNot monotonic
2023-08-01T09:52:54.279129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 15
 
2.5%
99 14
 
2.3%
125 11
 
1.8%
95 11
 
1.8%
105 11
 
1.8%
122 10
 
1.7%
119 10
 
1.7%
109 10
 
1.7%
111 10
 
1.7%
84 10
 
1.7%
Other values (119) 487
81.3%
ValueCountFrequency (%)
0 5
0.8%
44 1
 
0.2%
57 2
 
0.3%
61 1
 
0.2%
62 1
 
0.2%
67 1
 
0.2%
68 1
 
0.2%
71 4
0.7%
72 1
 
0.2%
73 3
0.5%
ValueCountFrequency (%)
198 1
 
0.2%
197 4
0.7%
196 3
0.5%
195 1
 
0.2%
194 3
0.5%
193 2
0.3%
191 1
 
0.2%
189 4
0.7%
188 2
0.3%
187 2
0.3%

Blood Pressure
Real number (ℝ)

Distinct44
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.732888
Minimum0
Maximum122
Zeros28
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size25.5 KiB
2023-08-01T09:52:54.591445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q164
median70
Q380
95-th percentile90
Maximum122
Range122
Interquartile range (IQR)16

Descriptive statistics

Standard deviation19.335675
Coefficient of variation (CV)0.2813162
Kurtosis5.2588834
Mean68.732888
Median Absolute Deviation (MAD)8
Skewness-1.8746617
Sum41171
Variance373.86833
MonotonicityNot monotonic
2023-08-01T09:52:54.910605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
70 46
 
7.7%
68 41
 
6.8%
74 38
 
6.3%
72 37
 
6.2%
64 37
 
6.2%
78 31
 
5.2%
80 30
 
5.0%
66 29
 
4.8%
76 29
 
4.8%
0 28
 
4.7%
Other values (34) 253
42.2%
ValueCountFrequency (%)
0 28
4.7%
24 1
 
0.2%
30 2
 
0.3%
40 1
 
0.2%
44 3
 
0.5%
46 1
 
0.2%
48 5
 
0.8%
50 10
 
1.7%
52 8
 
1.3%
54 8
 
1.3%
ValueCountFrequency (%)
122 1
 
0.2%
110 3
0.5%
108 2
0.3%
104 2
0.3%
102 1
 
0.2%
100 2
0.3%
98 3
0.5%
96 3
0.5%
95 1
 
0.2%
94 2
0.3%

Blood Work Result-2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.562604
Minimum0
Maximum99
Zeros175
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size25.5 KiB
2023-08-01T09:52:55.218566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation16.017622
Coefficient of variation (CV)0.77896855
Kurtosis-0.31425268
Mean20.562604
Median Absolute Deviation (MAD)12
Skewness0.16406327
Sum12317
Variance256.56422
MonotonicityNot monotonic
2023-08-01T09:52:55.545262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175
29.2%
32 25
 
4.2%
30 24
 
4.0%
33 17
 
2.8%
23 17
 
2.8%
28 17
 
2.8%
31 15
 
2.5%
18 15
 
2.5%
15 14
 
2.3%
27 14
 
2.3%
Other values (41) 266
44.4%
ValueCountFrequency (%)
0 175
29.2%
7 2
 
0.3%
8 2
 
0.3%
10 4
 
0.7%
11 5
 
0.8%
12 6
 
1.0%
13 8
 
1.3%
14 6
 
1.0%
15 14
 
2.3%
16 6
 
1.0%
ValueCountFrequency (%)
99 1
 
0.2%
63 1
 
0.2%
60 1
 
0.2%
56 1
 
0.2%
54 2
0.3%
52 2
0.3%
51 1
 
0.2%
50 3
0.5%
49 2
0.3%
48 2
0.3%

Blood Work Result-3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct164
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.460768
Minimum0
Maximum846
Zeros289
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size25.5 KiB
2023-08-01T09:52:55.845435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36
Q3123.5
95-th percentile293.7
Maximum846
Range846
Interquartile range (IQR)123.5

Descriptive statistics

Standard deviation116.57618
Coefficient of variation (CV)1.467091
Kurtosis8.0889556
Mean79.460768
Median Absolute Deviation (MAD)36
Skewness2.4015846
Sum47597
Variance13590.005
MonotonicityNot monotonic
2023-08-01T09:52:56.138634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 289
48.2%
140 8
 
1.3%
130 7
 
1.2%
105 7
 
1.2%
94 6
 
1.0%
115 5
 
0.8%
56 5
 
0.8%
120 5
 
0.8%
210 5
 
0.8%
76 5
 
0.8%
Other values (154) 257
42.9%
ValueCountFrequency (%)
0 289
48.2%
14 1
 
0.2%
18 2
 
0.3%
23 2
 
0.3%
25 1
 
0.2%
29 1
 
0.2%
32 1
 
0.2%
36 3
 
0.5%
37 2
 
0.3%
38 1
 
0.2%
ValueCountFrequency (%)
846 1
0.2%
744 1
0.2%
680 1
0.2%
600 1
0.2%
579 1
0.2%
545 1
0.2%
543 1
0.2%
495 2
0.3%
485 1
0.2%
480 1
0.2%

Body mass index
Real number (ℝ)

Distinct233
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.920033
Minimum0
Maximum67.1
Zeros9
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size25.5 KiB
2023-08-01T09:52:56.432101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.1
Q127.1
median32
Q336.55
95-th percentile45.02
Maximum67.1
Range67.1
Interquartile range (IQR)9.45

Descriptive statistics

Standard deviation8.0082273
Coefficient of variation (CV)0.25088405
Kurtosis3.2610266
Mean31.920033
Median Absolute Deviation (MAD)4.7
Skewness-0.40525495
Sum19120.1
Variance64.131705
MonotonicityNot monotonic
2023-08-01T09:52:56.742038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 11
 
1.8%
31.6 11
 
1.8%
33.3 10
 
1.7%
31.2 9
 
1.5%
0 9
 
1.5%
29.7 8
 
1.3%
33.6 7
 
1.2%
30.8 7
 
1.2%
32.9 7
 
1.2%
30.1 7
 
1.2%
Other values (223) 513
85.6%
ValueCountFrequency (%)
0 9
1.5%
18.2 3
 
0.5%
18.4 1
 
0.2%
19.1 1
 
0.2%
19.3 1
 
0.2%
19.4 1
 
0.2%
19.6 3
 
0.5%
19.9 1
 
0.2%
20 1
 
0.2%
20.4 2
 
0.3%
ValueCountFrequency (%)
67.1 1
0.2%
59.4 1
0.2%
55 1
0.2%
53.2 1
0.2%
52.9 1
0.2%
52.3 2
0.3%
50 1
0.2%
49.7 1
0.2%
48.8 1
0.2%
48.3 1
0.2%

Blood Work Result-4
Real number (ℝ)

Distinct437
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48118698
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.5 KiB
2023-08-01T09:52:57.033786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.1418
Q10.248
median0.383
Q30.647
95-th percentile1.1279
Maximum2.42
Range2.342
Interquartile range (IQR)0.399

Descriptive statistics

Standard deviation0.33755235
Coefficient of variation (CV)0.70149934
Kurtosis6.1146739
Mean0.48118698
Median Absolute Deviation (MAD)0.171
Skewness1.9894723
Sum288.231
Variance0.11394159
MonotonicityNot monotonic
2023-08-01T09:52:57.340640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.254 6
 
1.0%
0.258 5
 
0.8%
0.268 4
 
0.7%
0.687 4
 
0.7%
0.299 4
 
0.7%
0.237 4
 
0.7%
0.259 4
 
0.7%
0.238 4
 
0.7%
0.207 4
 
0.7%
0.151 3
 
0.5%
Other values (427) 557
93.0%
ValueCountFrequency (%)
0.078 1
0.2%
0.084 1
0.2%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.2%
0.092 1
0.2%
0.096 1
0.2%
0.101 1
0.2%
0.102 1
0.2%
0.107 1
0.2%
ValueCountFrequency (%)
2.42 1
0.2%
2.329 1
0.2%
2.288 1
0.2%
2.137 1
0.2%
1.893 1
0.2%
1.781 1
0.2%
1.731 1
0.2%
1.699 1
0.2%
1.6 1
0.2%
1.476 1
0.2%

Age
Real number (ℝ)

Distinct50
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.290484
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.5 KiB
2023-08-01T09:52:57.646462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q340
95-th percentile58.1
Maximum81
Range60
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.828446
Coefficient of variation (CV)0.35531012
Kurtosis0.69227058
Mean33.290484
Median Absolute Deviation (MAD)7
Skewness1.1523529
Sum19941
Variance139.91213
MonotonicityNot monotonic
2023-08-01T09:52:57.941489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 52
 
8.7%
21 52
 
8.7%
25 40
 
6.7%
24 37
 
6.2%
23 27
 
4.5%
29 27
 
4.5%
28 26
 
4.3%
26 25
 
4.2%
27 21
 
3.5%
41 20
 
3.3%
Other values (40) 272
45.4%
ValueCountFrequency (%)
21 52
8.7%
22 52
8.7%
23 27
4.5%
24 37
6.2%
25 40
6.7%
26 25
4.2%
27 21
3.5%
28 26
4.3%
29 27
4.5%
30 16
 
2.7%
ValueCountFrequency (%)
81 1
 
0.2%
72 1
 
0.2%
69 1
 
0.2%
67 3
0.5%
66 3
0.5%
65 3
0.5%
64 1
 
0.2%
63 3
0.5%
62 4
0.7%
61 2
0.3%

Insurance
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size25.5 KiB
1
411 
0
188 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Length

2023-08-01T09:52:58.233724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T09:52:58.486039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Most occurring characters

ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common 599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Sepssis
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size25.5 KiB
Negative
391 
Positive
208 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4792
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowPositive
4th rowNegative
5th rowPositive

Common Values

ValueCountFrequency (%)
Negative 391
65.3%
Positive 208
34.7%

Length

2023-08-01T09:52:58.699641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T09:52:58.967959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
negative 391
65.3%
positive 208
34.7%

Most occurring characters

ValueCountFrequency (%)
e 990
20.7%
i 807
16.8%
t 599
12.5%
v 599
12.5%
N 391
 
8.2%
g 391
 
8.2%
a 391
 
8.2%
P 208
 
4.3%
o 208
 
4.3%
s 208
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4193
87.5%
Uppercase Letter 599
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 990
23.6%
i 807
19.2%
t 599
14.3%
v 599
14.3%
g 391
 
9.3%
a 391
 
9.3%
o 208
 
5.0%
s 208
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
N 391
65.3%
P 208
34.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4792
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 990
20.7%
i 807
16.8%
t 599
12.5%
v 599
12.5%
N 391
 
8.2%
g 391
 
8.2%
a 391
 
8.2%
P 208
 
4.3%
o 208
 
4.3%
s 208
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 990
20.7%
i 807
16.8%
t 599
12.5%
v 599
12.5%
N 391
 
8.2%
g 391
 
8.2%
a 391
 
8.2%
P 208
 
4.3%
o 208
 
4.3%
s 208
 
4.3%

Interactions

2023-08-01T09:52:49.605605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:32.139488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:34.180316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:37.235310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:39.553627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:42.812907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:45.112835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:47.128268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:49.981479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:32.391454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:34.455693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:37.667417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:40.402627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:43.081686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:45.380206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:47.374983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:50.398236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:32.662393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:34.816361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:38.045219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:40.650140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:43.654743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:45.643286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:47.631702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:51.114318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:32.922998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:35.179491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:38.283655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:40.912434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:43.912906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:45.887403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:47.886159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:51.539943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:33.164091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:35.611739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:38.528439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:41.267147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:44.177323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:46.146857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:48.205049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:51.810199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:33.392374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:36.006774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:38.779037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:41.699847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:44.400895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:46.375737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:48.477434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:52.063942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:33.676184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:36.420546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:39.059220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:41.957516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:44.639668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:46.637830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:48.866739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:52.323812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:33.926374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:36.810302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:39.310043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:42.383165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:44.877413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:46.876008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T09:52:49.193060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-01T09:52:59.156043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Plasma glucoseBlood Work Result-1Blood PressureBlood Work Result-2Blood Work Result-3Body mass indexBlood Work Result-4AgeInsuranceSepssis
Plasma glucose1.0000.1410.172-0.083-0.1130.008-0.0630.6040.0440.214
Blood Work Result-10.1411.0000.2190.0530.2180.2230.0990.2950.0000.473
Blood Pressure0.1720.2191.0000.104-0.0160.287-0.0080.3410.0000.156
Blood Work Result-2-0.0830.0530.1041.0000.5400.4280.164-0.0810.0000.211
Blood Work Result-3-0.1130.218-0.0160.5401.0000.1720.238-0.1030.0000.197
Body mass index0.0080.2230.2870.4280.1721.0000.1170.1350.0000.325
Blood Work Result-4-0.0630.099-0.0080.1640.2380.1171.0000.0420.0000.176
Age0.6040.2950.341-0.081-0.1030.1350.0421.0000.0000.308
Insurance0.0440.0000.0000.0000.0000.0000.0000.0001.0000.042
Sepssis0.2140.4730.1560.2110.1970.3250.1760.3080.0421.000

Missing values

2023-08-01T09:52:52.657000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-01T09:52:53.051065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Plasma glucoseBlood Work Result-1Blood PressureBlood Work Result-2Blood Work Result-3Body mass indexBlood Work Result-4AgeInsuranceSepssis
ID
ICU20001061487235033.60.627500Positive
ICU2000111856629026.60.351310Negative
ICU2000128183640023.30.672321Positive
ICU20001318966239428.10.167211Negative
ICU2000140137403516843.12.288331Positive
ICU2000155116740025.60.201301Negative
ICU20001637850328831.00.248260Positive
ICU2000171011500035.30.134291Negative
ICU2000182197704554330.50.158531Positive
ICU200019812596000.00.232541Positive
Plasma glucoseBlood Work Result-1Blood PressureBlood Work Result-2Blood Work Result-3Body mass indexBlood Work Result-4AgeInsuranceSepssis
ID
ICU20059907300021.10.342250Negative
ICU200600111118440046.80.925450Positive
ICU2006012112785014039.40.175240Negative
ICU2006023132800034.40.402440Positive
ICU200603282522211528.51.699250Negative
ICU2006046123724523033.60.733340Negative
ICU2006050188821418532.00.682221Positive
ICU200606067760045.30.194461Negative
ICU20060718924192527.80.559210Negative
ICU2006081173740036.80.088381Positive